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/*=========================================================================
*
* Copyright NumFOCUS
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* https://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkDiscreteGaussianDerivativeImageFilter_h
#define itkDiscreteGaussianDerivativeImageFilter_h
#include "itkImageToImageFilter.h"
#include "itkImage.h"
namespace itk
{
/**
* \class DiscreteGaussianDerivativeImageFilter
* \brief Calculates image derivatives using discrete derivative gaussian kernels.
* This filter calculates Gaussian derivative by separable convolution of an image
* and a discrete Gaussian derivative operator (kernel).
*
* The Gaussian operators used here were described by Tony Lindeberg (Discrete
* Scale-Space Theory and the Scale-Space Primal Sketch. Dissertation. Royal
* Institute of Technology, Stockholm, Sweden. May 1991.)
*
* The variance or standard deviation (sigma) will be evaluated as pixel units
* if SetUseImageSpacing is off (false) or as physical units if
* SetUseImageSpacing is on (true, default). The variance can be set
* independently in each dimension.
*
* When the Gaussian kernel is small, this filter tends to run faster than
* itk::RecursiveGaussianImageFilter.
*
* \author Ivan Macia, Vicomtech, Spain, https://www.vicomtech.org/en
*
* This implementation was taken from the Insight Journal paper:
* https://www.insight-journal.org/browse/publication/179
*
* \sa GaussianDerivativeOperator
* \sa Image
* \sa Neighborhood
* \sa NeighborhoodOperator
*
* \ingroup ImageEnhancement
* \ingroup ITKImageFeature
*/
template <typename TInputImage, typename TOutputImage>
class ITK_TEMPLATE_EXPORT DiscreteGaussianDerivativeImageFilter : public ImageToImageFilter<TInputImage, TOutputImage>
{
public:
ITK_DISALLOW_COPY_AND_MOVE(DiscreteGaussianDerivativeImageFilter);
/** Standard class type aliases. */
using Self = DiscreteGaussianDerivativeImageFilter;
using Superclass = ImageToImageFilter<TInputImage, TOutputImage>;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(DiscreteGaussianDerivativeImageFilter);
/** Image type information. */
using InputImageType = TInputImage;
using OutputImageType = TOutputImage;
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
using OutputPixelType = typename TOutputImage::PixelType;
using OutputInternalPixelType = typename TOutputImage::InternalPixelType;
using InputPixelType = typename TInputImage::PixelType;
using InputInternalPixelType = typename TInputImage::InternalPixelType;
/** Extract some information from the image types. Dimensionality
* of the two images is assumed to be the same. */
static constexpr unsigned int ImageDimension = TOutputImage::ImageDimension;
/** Typedef of double containers */
using ArrayType = FixedArray<double, Self::ImageDimension>;
/** Array for storing desired order of derivatives */
using OrderArrayType = FixedArray<unsigned int, Self::ImageDimension>;
/** Order of derivatives in each dimension. Sets the derivative order
* independently for each dimension, but see also
* SetOrder(const unsigned int v). The default is 1 in each dimension. */
itkSetMacro(Order, OrderArrayType);
itkGetConstMacro(Order, const OrderArrayType);
/** The variance for the discrete Gaussian kernel. Sets the variance
* independently for each dimension, but
* see also SetVariance(const double v). The default is 0.0 in each
* dimension. If UseImageSpacing is true, the units are the physical units
* of your image. If UseImageSpacing is false then the units are
* pixels. */
itkSetMacro(Variance, ArrayType);
itkGetConstMacro(Variance, const ArrayType);
/** The algorithm will size the discrete kernel so that the error
* resulting from truncation of the kernel is no greater than
* MaximumError. The default is 0.01 in each dimension. */
itkSetMacro(MaximumError, ArrayType);
itkGetConstMacro(MaximumError, const ArrayType);
/** Set the kernel to be no wider than MaximumKernelWidth pixels,
* even if MaximumError demands it. The default is 32 pixels. */
itkGetConstMacro(MaximumKernelWidth, int);
itkSetMacro(MaximumKernelWidth, int);
/** \brief Set/Get number of pieces to divide the input for the
* internal composite pipeline. The upstream pipeline will not be
* effected.
*
* The default value is $ImageDimension^2$.
*
* This parameter was introduced to reduce the memory used by images
* internally, at the cost of performance.
*/
itkSetMacro(InternalNumberOfStreamDivisions, unsigned int);
itkGetConstMacro(InternalNumberOfStreamDivisions, unsigned int);
/** Convenience Set methods for setting all dimensional parameters
* to the same values.
*/
/*@{*/
void
SetOrder(const typename OrderArrayType::ValueType v)
{
OrderArrayType a;
a.Fill(v);
this->SetOrder(a);
}
void
SetVariance(const typename ArrayType::ValueType v)
{
ArrayType a;
a.Fill(v);
this->SetVariance(a);
}
void
SetMaximumError(const typename ArrayType::ValueType v)
{
ArrayType a;
a.Fill(v);
this->SetMaximumError(a);
}
/*@}*/
/** Set/Get whether or not the filter will use the spacing of the input
image in its calculations. Default is ImageSpacingOn. */
itkSetMacro(UseImageSpacing, bool);
itkGetConstMacro(UseImageSpacing, bool);
itkBooleanMacro(UseImageSpacing);
/** Set/Get the flag for calculating scale-space normalized derivatives.
* Normalized derivatives are obtained multiplying by the scale
* parameter t. */
itkSetMacro(NormalizeAcrossScale, bool);
itkGetConstMacro(NormalizeAcrossScale, bool);
itkBooleanMacro(NormalizeAcrossScale);
#ifdef ITK_USE_CONCEPT_CHECKING
// Begin concept checking
itkConceptMacro(OutputHasNumericTraitsCheck, (Concept::HasNumericTraits<OutputPixelType>));
// End concept checking
#endif
protected:
DiscreteGaussianDerivativeImageFilter()
{
m_Order.Fill(1);
m_Variance.Fill(0.0);
m_MaximumError.Fill(0.01);
m_MaximumKernelWidth = 32;
m_UseImageSpacing = true;
m_NormalizeAcrossScale = false;
m_InternalNumberOfStreamDivisions = ImageDimension * ImageDimension;
}
~DiscreteGaussianDerivativeImageFilter() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
/** DiscreteGaussianDerivativeImageFilter needs a larger input requested region
* than the output requested region (larger by the size of the
* Gaussian kernel). As such, DiscreteGaussianDerivativeImageFilter needs to
* provide an implementation for GenerateInputRequestedRegion() in
* order to inform the pipeline execution model.
* \sa ImageToImageFilter::GenerateInputRequestedRegion() */
void
GenerateInputRequestedRegion() override;
/** Standard pipeline method. While this class does not implement a
* ThreadedGenerateData(), its GenerateData() delegates all
* calculations to an NeighborhoodOperatorImageFilter. Since the
* NeighborhoodOperatorImageFilter is multithreaded, this filter is
* multithreaded by default. */
void
GenerateData() override;
private:
/** The order of the derivatives in each dimensional direction. */
OrderArrayType m_Order{};
/** The variance of the gaussian blurring kernel in each dimensional
direction. */
ArrayType m_Variance{};
/** The maximum error of the gaussian blurring kernel in each dimensional
* direction. For definition of maximum error, see GaussianOperator.
* \sa GaussianOperator */
ArrayType m_MaximumError{};
/** Maximum allowed kernel width for any dimension of the discrete Gaussian
approximation */
int m_MaximumKernelWidth{};
/** Flag to indicate whether to use image spacing */
bool m_UseImageSpacing{};
/** Flag for scale-space normalization of derivatives. */
bool m_NormalizeAcrossScale{};
/** Number of pieces to divide the input on the internal composite
pipeline. The upstream pipeline will not be effected. */
unsigned int m_InternalNumberOfStreamDivisions{};
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
# include "itkDiscreteGaussianDerivativeImageFilter.hxx"
#endif
#endif
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